We’ve read your posts and heard your feedback. 

1. We’re postponing the announced billing change for self-hosted GitHub Actions to take time to re-evaluate our approach. 
2. We are continuing to reduce hosted-runners prices by up to 39% on January 1, 2026. 

We have real costs in running the Actions control plane. We are also making investments into self-hosted runners so they work at scale in customer environments, particularly for complex enterprise scenarios. While this context matters, we missed the mark with this change by not including more of you in our planning. 

We need to improve GitHub Actions. We’re taking more time to meet and listen closely to developers, customers, and partners to start. We’ve also opened a [discussion to collect more direct feedback](https://github.com/orgs/community/discussions/182186) and will use that feedback to inform the GitHub Actions roadmap. We’re working hard to earn your trust through consistent delivery across GitHub Actions and the entire platform. 

---
__Below is the original announcement from 12/16/25__

We’re announcing updates to our pricing and product models for GitHub Actions. 

Historically, self-hosted runner customers were able to leverage much of GitHub Actions’ infrastructure and services at no cost. This meant that the cost of maintaining and evolving these essential services was largely being subsidized by the prices set for GitHub-hosted runners. By updating our pricing, we’re aligning costs more closely with usage and the value delivered to every Actions user,  while fueling further innovation and investment across the platform. The vast majority of users, especially individuals and small teams, will see no price increase.

We will have a GitHub Actions pricing calculator available where you will know how much you will be charged. You can see the [Actions pricing calculator](https://github.com/pricing/calculator#actions) to estimate your future costs. 96% of customers will see no change to their bill. Of the 4% of Actions users impacted by this change, 85% of this cohort will see their Actions bill decrease and the remaining 15% who are impacted across all face a median increase around $13. 

GitHub Actions will remain free for public repositories. In 2025, we saw developers use 11.5 billion total Actions minutes in public projects for free (\~$184 million) and we will continue to invest in Actions to provide a fast, reliable, and predictable experience for our users. 

## Background  
When we shipped Actions in 2018, we had no idea how popular it would become. By early 2024, the platform was running about 23 million jobs per day and our existing architecture couldn’t reliably support our growth curve. In order to increase feature velocity, we first needed to improve reliability and modernize the legacy frameworks that supported GitHub Actions. 

[Our solution was to re-architect the core backend services powering GitHub Actions jobs](https://github.blog/news-insights/product-news/lets-talk-about-github-actions/) and runners with the goals of improving uptime and resilience against infrastructure issues, enhancing performance, reducing internal throttles, and leveraging GitHub’s broader platform investments and developer experience improvements. This work is paying off by helping us handle our current scale, even as we work through the last pieces of stabilizing our new platform.

Since August, all GitHub Actions jobs have run on our new architecture, which handles 71 million jobs per day (over 3x from where we started). Individual enterprises are able to start 7x more jobs per minute than our previous architecture could support. 

As with any product, our goal at GitHub has been to meet customer needs while providing enterprises with flexibility and transparency.

This change better supports a world where CI/CD must be faster and more reliable, better caching, more workflow flexibility, rock-solid reliability, and strengthens the core experience while positioning GitHub Actions to power GitHub’s open, secure platform for agentic workloads. 

## What’s changing?

### Lower prices for GitHub-hosted runners

Starting today, we’re charging fairly for Actions across the board which reduces the price of GItHub Hosted Runners and the price the average GitHub customer pays. And we’re **reducing the net cost of GitHub-hosted runners by up to 39%**, depending on which machine type is used.

This reduction is driven by a \~40% price reduction across all runner sizes, paired with the addition of a new $0.002 per-minute GitHub Actions cloud platform charge. For GitHub-hosted runners, the new Actions cloud platform charge is **already included into the reduced meter price.** 

Standard GitHub-hosted or self-hosted runner usage on **public repositories will remain free**. **GitHub Enterprise Server pricing is not impacted** by this change.

The price reduction you will see in your account depends on the types of machines that you use most frequently – smaller runners will have a smaller relative price reduction, larger runners will see a larger relative reduction.

This price reduction makes high-performance compute more accessible for both high-volume CI workloads and the agent jobs that rely on fast, secure execution environments.

For full pricing update details, see the updated Actions runner prices [in our documentation](https://docs.github.com/billing/reference/actions-runner-pricing).

This price change will **go into effect on January 1, 2026**.

### Introduction of the GitHub Actions cloud platform charge

We are introducing a **$0.002 per-minute Actions cloud platform** **charge** for all Actions workflows across GitHub-hosted and self-hosted runners. The new listed GitHub-runner rates include this charge. This will not impact Actions usage in public repositories or GitHub Enterprise Server customers.  

This aligns pricing to match consumption patterns and ensures consistent service quality as usage grows across both hosting modalities.

This will impact **self-hosted runner pricing starting March 1, 2026\.**

### Deepened investment in the Actions self-hosted experience

We are increasing our investment into our self-hosted experience to ensure that we can provide autoscaling for scenarios beyond just Linux containers. This will include new approaches to scaling, new platform support, Windows support, and more as we move through the next 12 months. Here’s a preview of what to expect in the new year:

#### GitHub Scale Set Client

This [new client](https://github.com/github/roadmap/issues/1192) provides enterprises with a lightweight Go SDK to build custom autoscaling solutions without the complexity of Kubernetes or reliance on ARC. It integrates seamlessly with existing infrastructure—containers, virtual machines, cloud instances, or bare metal—while managing job queuing, secure configuration, and intelligent scaling logic. Customers gain a supported path to implement flexible autoscaling, reduce setup friction, and extend GitHub Actions beyond workflows to scenarios such as self-hosted Dependabot and Copilot Coding Agent.

#### Multi-label support

We are reintroducing [multi-label functionality](https://github.com/github/roadmap/issues/1195) for both GitHub-hosted larger runners and self-hosted runners, including those managed by Actions Runner Controller (ARC) and the new Scale Set Client.

#### Actions Runner Controller 0.14.0 

[This upcoming release](https://github.com/github/roadmap/issues/1194) introduces major quality-of-life improvements, including refined Helm charts for easier Docker configuration, enhanced logging, updated metrics, and formalized versioning requirements. It also announces the deprecation of legacy ARC, providing a clear migration path to a more reliable and maintainable architecture. Customers benefit from simplified setup, improved observability, and confidence in long-term support, reducing operational friction and improving scalability.

#### Actions Data Stream

The [Actions Data Stream](https://github.com/github/roadmap/issues/1193) will deliver a near real-time, authoritative feed of GitHub Actions workflow and job event data, including metadata such as the version of the action that was executed on any given workflow run. This capability enhances observability and troubleshooting by enabling organizations to integrate event data into monitoring and analytics systems for compliance and operational insights. By providing structured, high-fidelity data at scale, it eliminates reliance on manual log parsing and empowers teams to proactively manage reliability and performance.

## Why this matters

Agents are expanding what teams can automate—but CI/CD remains the heartbeat of modern software delivery. These updates enable both a faster, more reliable CI/CD experience for every developer, and a scalable, flexible, secure execution layer to power GitHub’s agentic platform.

Our goal is to ensure GitHub Actions continues to meet the needs of the largest enterprises and of individual developers alike, with clear pricing, stronger performance, and a product direction built for the next decade of software development.

## FAQ

**Why am I being charged to use my own hardware?**  
Historically, self-hosted runner customers were able to leverage much of GitHub Actions’ infrastructure and services at no cost. This meant that the cost of maintaining and evolving these essential services was largely being subsidized by the prices set for GitHub-hosted runners. By updating our pricing, we’re aligning costs more closely with usage and the value delivered to every Actions user,  while fueling further innovation and investment across the platform. The vast majority of users, especially individuals and small teams, will see no price increase.

You can see the [Actions pricing calculator](https://github.com/pricing/calculator#actions) to estimate your future costs.

**What are the new GitHub-hosted runner rates?**  
See the GitHub Actions [runner pricing reference](https://docs.github.com/billing/reference/actions-runner-pricing) for the updated rates that will go into effect on January 1, 2026\. These listed rates include the new $0.002 per-minute Actions cloud platform charge.

**Q: Why is .002/minute the right price for self-hosted runners on cloud?**  
We determined per-minute was deemed the most fair and accurate by our users, and compared to other self-hosted CI solutions in the market. We believe this is a sustainable option that will not deeply impact our lightly- nor heavily-active customers, while still delivering fast, flexible workloads for the best end user experience. 

**Which job execution scenarios for GitHub Actions are affected by this pricing change?**

* Jobs that run in private repositories and use standard GitHub-hosted or self-hosted runners  
* Any jobs running on larger GitHub-hosted runners

Standard GitHub-hosted or self-hosted runner usage on public repositories will remain free. GitHub Enterprise Server pricing is not impacted by this change.

**When will this pricing change take effect?**

The price decrease for GitHub-hosted runners will take effect on January 1, 2026\. The new charge for self-hosted runners will apply beginning on March 1, 2026\. The price changes will impact all customers on these dates.

**Will the free usage quota available in my plan change?**  
Beginning March 1, 2026, self-hosted runners will be included within your free usage quota, and will consume available usage based on list price the same way that Linux, Windows, and MacOS standard runners work today.

**Will self-hosted runner usage consume from my free usage minutes?**  
Yes, billable self-hosted runner usage will be able to consume minutes from the free quota associated with your plan.

**How does this pricing change affect customers on GitHub Enterprise Server?**

This pricing change does not affect customers using GitHub Enterprise Server. Customers running Actions jobs on self-hosted runners on GitHub Enterprise Server may continue to host, manage, troubleshoot and use Actions on and in conjunction with their implementation free of charge. 

**Can I bill my self-hosted runner usage on private repositories through Azure?**

Yes, as long as you have an active Azure subscription ID associated with your GitHub Enterprise or Organization(s).

**What is the overall impact of this change to GitHub customers?**

96% of customers will see no change to their bill. Of the 4% of Actions users impacted by this change, 85% of this cohort will see their Actions bill decrease and the remaining 15% who are impacted across all face a median increase around $13. 

**Did GitHub consider how this impacts individual developers, not just Enterprise scale customers of GitHub?**  
From our individual users (free & Pro plans) of those who used GitHub Actions in the last month in private repos only 0.09% would end up with a price increase, with a median increase of under $2 a month. Note that this impact is after these users have made use of their included minutes in their plans today, entitling them to over 33 hours of included GitHub compute, and this has no impact on their free use of public repos. A further 2.8% of this total user base will see a decrease in their monthly cost as a result of these changes. The rest are unimpacted by this change.

**How can I figure out what my new monthly cost for Actions looks like?**

GitHub Actions provides [detailed usage reports for the current and prior year](https://docs.github.com/billing/how-tos/products/view-productlicense-use). You can use this prior usage alongside the [rate changes](https://docs.github.com/billing/reference/actions-runner-pricing) that will be introduced in January and March to estimate cost under the new pricing structure. We have created a [Python script](https://docs.github.com/billing/tutorials/estimate-actions-costs) to help you leverage [full usage reports](https://docs.github.com/billing/how-tos/products/view-productlicense-use#downloading-usage-reports) to calculate your expected cost after the price updates.

We have also updated our [Actions pricing calculator](https://github.com/pricing/calculator#actions), making it easier to estimate your future costs, particularly if your historical usage is limited or not representative of expected future usage.

## Additional resources

* See the [GitHub Actions runner pricing documentation](https://docs.github.com/billing/reference/actions-runner-pricing) for the new GitHub-hosted runner rates effective January 1, 2026\.  
* For more details on upcoming GitHub Actions releases, see the [GitHub public roadmap](https://github.com/orgs/github/projects/4247).  
* For help estimating your expected Actions usage cost, use the newly updated [Actions pricing calculator](https://github.com/pricing/calculator#actions).  
* To see your current or historical Actions usage, see our documentation for [viewing and downloading detailed usage reports](https://docs.github.com/billing/how-tos/products/view-productlicense-use).  
* If you are interested in moving existing self-hosted runner usage to GitHub-hosted runners, see the [SHR to GHR migration guide](http://docs.github.com/actions/tutorials/migrate-to-github-runners) in our documentation.

TLDR: We’re postponing the announced billing change for self-hosted GitHub Actions to take time to re-evaluate our approach. We are continuing to reduce hosted-runners prices by up to 39% on January 1, 2026.

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December ‘25 enterprise roundup

Published via [GitHub Executive Insights](https://github.com/solutions/executive-insights "Executive insights, curated just for you") | Authored by [Matt Nigh](https://github.com/mattnigh), Program Manager Director of AI for Everyone

In this series reflecting on [GitHub’s internal playbook for AI adoption](https://resources.github.com/enterprise/ai-powered-workforce-playbook/) we have covered how to establish clear guardrails, provide learning opportunities, and activate a network of advocates. Now we’re digging into how you scale day-to-day learning to ensure valuable knowledge doesn’t vanish into private chats.

The solution lies in Communities of Practice (CoPs). If your AI Advocates are the catalysts, CoPs are the chemical reaction. They are the forums where individual discoveries become collective intelligence, and where your company's AI fluency truly grows.

This guide shows you how to move from scattered, one-off conversations to intentional, high-impact AI communities.

## The Problem: Fragmented and lost learnings

AI experiments are already happening inside DMs, in private team Slack channels, and buried in slide decks. But most of that insight disappears fast. A clever prompt is forgotten. A smart workaround is shared once and never again. Someone solves a problem, but the next team has to rebuild from scratch.

Without a system to surface and spread these moments, you get duplication instead of acceleration. A solid CoP strategy fixes that. It gives your organization a shared, searchable, and social space to learn together.

## A blueprint for your communities

Cultivating a CoP network is an act of intentional community management. It’s about providing the right structure and then actively nurturing the conversations within it.

### Step 1: Start with a hub-and-spoke model 

Don't start with a single, monolithic `#ai` channel. It will quickly become too noisy to be useful. Instead, launch with a "hub-and-spoke" model to serve different needs.

* **The hub:** A general-purpose entry point. Create a single, company-wide channel (e.g., `#how-do-i-ai`) to serve as the front door for your entire AI ecosystem. This is the place for broad announcements, general questions, and for employees who don't know where else to go.  
* **The spokes**: These should be deep-dive, specialist communities. Launch one or two specialized channels for your most critical user groups. A developer-focused community (e.g., `#copilot-power-users`) is almost always a good place to start. As adoption grows, you can spin up new "spokes" for other functions like marketing, sales, or finance, as demand grows.

### Step 2: Assign clear ownership and a simple charter 

Every community needs a designated leader and a clear purpose. This is a perfect role for your AI Advocates.

* **Assign a leader:** Ask an Advocate from the relevant department to be the official owner or facilitator of each community.  
* **Write a charter:** The leader should write a simple, one-paragraph charter pinned to the channel. It should answer three questions: What is the purpose of this community? Who is it for? What are the key topics of discussion? This prevents confusion and keeps the conversation focused.

### Step 3: Actively manage and sustain momentum 

Creating channels is easy, keeping them valuable is hard. The AI Program DRI, in partnership with the community leaders, must actively work to sustain momentum.

* **Showcase and amplify:** Create a rhythm for finding and celebrating the best content from your CoPs. This could be a weekly "best of" post that summarizes the most interesting use cases or a "discussion firehose" that automatically shares tagged posts from various repos into a central channel.  
* **Run community programming:** Use the communities as the primary venue for your AI events. Announce new L\&D opportunities, host tool-specific Q\&As, and run your office hours within the relevant channels to drive engagement.  
* **Facilitate connections:** The DRI and Advocates should actively monitor conversations to act as connectors. When one person asks a question, they can tag another employee who they know has the answer. This active facilitation builds the network and reinforces the value of the community.  
* **Encourage leadership participation:** Momentum is not just a grassroots effort; it needs air cover. Leaders and senior ICs play a critical role in sustaining community health. They should be encouraged not only to direct their teams to these communities but to participate themselves. A senior engineer asking a question in the developer channel or a VP celebrating a use case shared in the general hub provides powerful social proof, signaling that these communities are a valuable and important place to spend time.

### Step 4: Supercharge your communities with events 

Async channels are the foundation, but live and structured events are what turn a community into a vibrant hub of innovation. These events create focal points for learning and strengthen the connections between members.

* **Host virtual learning events:** Organize company-wide events like a "Day of Learning" or a hackathon focused on solving specific business problems with AI. These events, run through the CoPs, generate a burst of energy, collaboration, and a wealth of new use cases. Crucially, all sessions should be recorded and shared for asynchronous consumption, turning a one-time event into a lasting learning artifact.  
* **Run executive listening sessions:** Create a direct feedback loop by hosting regular, informal listening sessions between community leaders and executive sponsors. This gives leaders an unfiltered view of what's working, what's not, and how they can best support the company's AI and L\&D efforts. It also provides community leaders with a direct line to decision-makers, making them feel valued and empowered.  
* **Organize community leader offsites:** Bring your community leaders (your AI Advocates) together for a dedicated virtual offsite or summit. This provides a valuable opportunity for them to share best practices, align on goals for the next quarter, and build the personal relationships that are essential for a strong, self-sustaining network.

When you shift from a passive approach to actively nurturing community, those scattered conversations start to work together. You build a space where people learn from each other, knowledge flows more freely, and AI fluency spreads across your organization.

## Complementing CoPs with tooling

Your CoPs generate the knowledge, and these tools ensure it’s captured, refined, and shared effectively. Each tool play a distinct role in how knowledge is created, shared, and scaled across your organization.

* **Slack/Teams channels** are your CoP’s real-time pulse, where ideas come alive, problems are solved, and questions find quick answers.  
* **GitHub Discussions** (or similar) provide structure and continuity—a searchable home for insights you don’t want getting buried in chat.  
* **Internal wikis** are where best practices, reusable assets, and canonical guidance live on.

Here’s how to connect them:

* **Capture valuable insights:** When a conversation in chat surfaces a breakthrough, move it to GitHub Discussions. Add a clear title, summary, and tags like `prompt`, `copilot-tip`, or `use-case` which will also make content easy to discover later.  
* **Foster async participation:** GitHub Discussions support thoughtful input across time zones. They’re ideal for deeper problem-solving and design debates.  
* **Automate highlight sharing:** Use lightweight bots to summarize top GitHub Discussions on a regular basis or auto-post starred threads into CoP channels.  
* **Build cross-functional bridges:** Encourage cross-posting between functions, like surfacing a product team’s AI win in an engineering CoP, or vice versa.  
* **Promote graduation to wiki:** Once a discussion gains consensus or maturity, document it in your internal wiki. Include links to the original thread for context.

## **Make learning visible, scalable, and durable**

If you want AI adoption to stick, you need more than tools and training. You need a system that captures what people learn, connects those learnings across teams, and turns them into reusable knowledge.

Communities of Practice give you that system. They make day-to-day learning visible. They prevent repetition by sharing solutions and accelerate progress by connecting people solving similar problems. And when combined with structured tools like GitHub Discussions and internal wikis, they ensure your best ideas don’t get lost.

Don’t let AI learning stay locked in private chats or isolated teams. Build a network where insights are shared, refined, and documented. That’s how you move from scattered experimentation to coordinated transformation.

AI fluency at scale doesn’t happen by accident. It happens when you design for knowledge flow, sustain community energy, and make collective learning part of how your company works.

Your AI knowledge is vanishing in private chats. It's time to build Communities of Practice—the engine that transforms scattered individual insights into collective, scalable organizational intelligence.

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